Response Surface Optimization in Discharge Printing of Denim Using Potassium Permanganate as Oxidative Agent

Abstract

The present study was carried out to optimize discharge printing process for fashionable denim garments. Response surface methodology, involving a central composite design with three key factors, namely, potassium permanganate (KMnO₄) concentration, pH of the printing paste, and reaction time, was successfully employed. The objective of this work was to develop a cost-effective, value-added process for denim fabric, where losses in tensile and tear strengths were to be minimized, while whiteness effect of discharge was to be maximized. The optimum conditions for discharge printing with potassium permanganate were found to be pH 6, KMnO₄ concentration of 42 g/kg, and treatment time of 15 min. The validity of experimental values was found to be in good agreement with optimized combination of three variables.

Keywords

denim discharge printing indigo dye potassium permanganate response surface methodology

Textile substrates are printed using different styles such as direct, discharge, resist, and so forth. In the discharge style of printing, the dye of a ground-dyed fabric is discharged from the required design or pattern. This discharge can lead to a white or colored pattern, unless an undischargeable colorant is used in the discharge printing paste (Glover, 2005; Miles, 2003). The dye is discharged by a chemical cleavage of chromophore, a substance responsible for the visual effect of color (El-thalouth, Kantouch, Nassar, & El-hennawi, 2008). This chemical cleavage of chromophore is achievable either by reduction or oxidation reaction. On this basis, the compounds used for discharging the dyes are classified as oxidative and reductive agents. Common oxidative agents are potassium permanganate (KMnO₄), hydrogen peroxide, sodium hypochlorite, sodium perborate, and sodium percarbonate. Common reductive agents are thiourea dioxide, tin chloride, sodium dithionite, and formaldehyde sulfoxylates (Haggag, Ragheb, Abd El-Thalouth, Nassar, & El-Sayed, 2013). Microorganisms and enzymes are also used to discharge different dyes (Ragheb, Haggag, Rekaby, & Shahin, 2013). Enzymes such as laccase and glucose oxidase are prominent in the textile industry (Ragheb et al., 2013). However, enzymes are normally specific in nature and purpose; therefore, the nature and properties of the fabric and the dye play an important role in the selection of these enzymes (Haggag et al., 2013).

Denim fabric is normally made through a twill weave structure of dyed warp and gray weft yarns. The warp yarn is normally dyed with indigo, vat, and sulfur dyes. Indigo-dyed denim has the biggest share of the world’s denim jeans production (Adnan, 2010). Generally, sodium chlorate and sulphonated dimethylphenylbenzyl ammonium salts have been used to discharge indigo-dyed denim as oxidative agents in discharge printing (Lacasse & Baumann, 2004). Sodium formaldehyde sulfoxylate has been used on a commercial scale as a reductive agent in discharge printing (Alsberg & Liquorice, 1962). Among enzymes, laccase works in an oxidative way to discharge indigo-dyed fabric. There is still a need to study and optimize the discharge behavior of commonly available oxidative agents like KMnO₄.

The selection of discharging agents for printing denim is based on their activity against indigo dye. For our research, KMnO₄ was selected as the oxidative discharging agent for denim discharge printing. The discharge printing recipe and process conditions have been optimized by response surface methodology (RSM). Among various mathematical and statistical techniques used to design experiments, develop models, and evaluate several significant variables for optimization, RSM is the most popular (Demirel & Kayan, 2012). It is normally used for two types of designs: central composite design (CCD) and Box–Behnken design. The CCD is useful for successive experimentations to build a relationship between multiple input and output variables by avoiding a large number of unusual observations (Demirel & Kayan, 2012; Hussain, Ahmad, & Masood, 2013).

In the present research, the CCD has been used to optimize material concentration as well as conditions for obtaining maximum discharging and better physical properties through discharge printing of denim. Models have also been developed to predict the whiteness, tensile, and tear strength of discharge-printed denim fabric using KMnO₄ as the oxidative agent.

Method

Indigo-dyed, 100% cotton, 3/1 twill denim fabric, obtained from a local industry, was used for our research. The fabric properties are as follows: warp linear density: 7 Ne, weft linear density: 6 Ne, ends per inch: 62, picks per inch: 41, and areal density: 390 g/m². The denim fabric was desized using a solution containing 1.0 g/l amylase (Bactosol PHC, Archroma, Reinach, Switzerland) and 0.5 g/l wetting agent (Hostapal UH by Archroma Clariant) on a lab-scale Jigger machine (F-24 by F. Smith and Co. Ltd., England) for 45 min at 70°C, followed by hot washing and then cold rinsing.

The RSM statistical technique was used for the experimental design and optimization process of variables to enhance whiteness and minimize fabric strength losses. The RSM is a useful method for studying the effect of multiple variables that influence responses by varying them simultaneously and carrying out a limited number of experiments (Demirel & Kayan, 2012). The coded and actual levels of variables used in discharge printing are given in Table 1. The CCD experimental design of RSM was created using the Minitab 17 statistical software package, as shown in Table 2. There were three experimental variables, each with five levels: concentration of KMnO₄ (30, 40, 50, 60, and 70 g/kg), pH of the printing paste (4, 5, 6, 7, and 8), and reaction time (0, 5, 10, 15, and 20 min) after printing for 20 experimental runs. The middle level or center point was coded as 0, and the other four levels were coded as −2, −1, +1, and +2. Statistical analysis of all test results was also done with the Minitab. Response surface regression was used to evaluate the relationships between factors and their responses. Surface plots related with response surface regression were used to visualize the relationships of different factors with their responses. Analysis of variance (ANOVA) was performed to describe the statistical significance of factors, square, and interaction terms included in the regression models.

Table 1.

Coded and Actual Levels of Variables.

Factor Symbol	Factors	−2	−1	0	1	2
X ₁	Potassium permanganate concentration (g/kg)	30	40	50	60	70
X ₂	pH	4	5	6	7	8
X ₃	Time (min)	0	5	10	15	20

Table 2.

Central Composite Design for Discharge Printing.

S. No.	KMnO₄ (g/kg)	pH	Time (min)			Tensile strength				Tear strength
				Whiteness		Warp (N)		Weft (N)		Warp (cN)		Weft (cN)
				Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
1	40	5	5	64.31	2.0	570.13	4.5	464.69	5.2	5,821	7.5	5,154	10.4
2	60	5	5	61.09	1.8	472.45	5.5	381.74	4.4	5,027	8.9	4,782	7.0
3	40	7	5	55.03	1.7	621.57	7.9	507.91	7.2	6,056	8.5	5,252	8.9
4	60	7	5	56.29	0.9	501.02	6.3	405.11	6.2	5,782	2.6	5,135	5.0
5	40	5	15	70.02	2.0	555.00	6.3	441.90	3.9	5,664	7.9	5,076	5.6
6	60	5	15	66.52	1.8	465.22	4.5	368.12	4.6	4,909	10.8	4,723	10.8
7	40	7	15	57.27	1.8	603.55	7.2	496.31	7.0	5,762	11.4	5,213	8.0
8	60	7	15	55.77	1.0	485.24	6.2	390.82	3.6	5,390	6.0	4,939	3.6
9	30	6	10	58.22	0.4	655.50	4.8	519.21	5.3	6,174	8.7	5,311	8.9
10	70	6	10	57.12	1.3	450.00	5.1	365.99	5.9	4,753	4.0	4,645	7.0
11	50	4	10	72.04	1.1	476.02	5.8	378.89	4.9	5,145	9.6	4,841	9.6
12	50	8	10	52.55	1.1	575.40	6.5	480.51	3.7	5,762	9.2	5,213	6.2
13	50	6	0	59.82	1.4	556.21	7.8	449.69	3.6	5,586	10.4	5,115	9.5
14	50	6	20	65.72	3.1	508.10	6.3	412.89	5.6	5,321	9.5	4,880	11.5
15	50	6	10	63.22	0.7	538.05	5.0	431.50	5.6	5,488	7.2	5,066	8.5
16	50	6	10	61.91	1.0	542.02	6.0	445.20	4.1	5,664	4.4	5,017	1.7
17	50	6	10	61.96	0.8	550.22	3.2	436.11	5.2	5,586	9.5	5,037	7.0
18	50	6	10	63.76	1.2	547.00	5.9	435.23	5.3	5,625	7.0	5,076	8.5
19	50	6	10	62.96	0.3	532.14	5.2	439.21	6.1	5,644	11.5	5,037	6.1
20	50	6	10	64.09	0.5	539.01	4.6	431.45	5.5	5,605	8.9	5,056	7.8

Note. SD = standard deviation; KMnO₄ = potassium permanganate.

Denim fabric samples were printed using pastes containing different amount of KMnO₄, 25 g/kg of a synthetic thickener (Alco print PTF), and water on a flatbed screen printing machine. The pH was adjusted using acetic acid. Printing pastes were prepared according to the experimental design given in Table 2. When the paste was applied to the deep purple fabric, its color turned brown due to the transformation of KMnO₄ into manganese oxide (MnO₂). After printing, the samples were kept under ambient conditions for different intervals of time as mentioned in Table 2. Then, the fabric samples were subjected to treatment with a sodium bisulfite solution (1 g/l) at 70°C for 10 min at pH 6.5–7, followed by rinsing with cold water and then drying. The pH of the sodium bisulfite bath was adjusted using a sodium bicarbonate solution (0.05 M). Sodium bisulfite was used to reduce MnO₂ in water-soluble form of Mn²⁺, in order to get the effect of white discharge. The whiteness of textile fabrics including denim is achieved through bleaching (oxidation reaction), and while MnO₂ does not truly impart any sort of whiteness, it might stain bleached fabric, resulting in lower whiteness, if not properly removed. Hence, the removal of MnO₂ by changing it to a solubilized form (Mn²⁺) can be done with the help of sodium bisulfite, which is known for its ability to remove stains of MnO₂ by converting the MnO₂ into Mn²⁺ (water soluble; Chi et al., 2002).

After the discharge printing process, the samples were dried and conditioned under standard atmospheric conditions (25°C, 65% relative humidity [RH] for 4 hr) before testing. Then, the whiteness of the fabric samples from the discharged areas was measured on a spectrophotometer (7,000A, Color Eye Gretag Macbeth) according to American Society for Testing and Materials (ASTM) method ASTM E313 Standard Practice for Calculating Yellowness and Whiteness Indices from Instrumentally Measured Color Coordinates. The tensile strength of the discharged printed samples was measured according to ASTM D5034 Standard Test Method for Breaking Strength and Elongation of Textile Fabrics (Grab Test) on a Universal Tensile Strength tester (Lloyd, Ametek Co., USA). Tear strength was determined according to ASTM D1424 Standard Test Method for Tearing Strength of Fabrics by Falling-Pendulum Apparatus on an Elmendorf Tear Strength tester (1992, Daiei Kagaku Mfg. Co. Ltd., Japan). Whiteness was measured at three different places for each sample, while for tensile and tear strengths, three specimens were cut from each sample. Their mean values along with standard deviations are given in Table 2. All the experiments were done in triplicate; the results reported are averages of three concordant readings of each replicate specimen.

Results and Discussion

The ANOVA gives information about quadratic and interaction effects along with the normal linearized effects of the parameters. The variables with probability values (p values) less than .05 are considered to have a significant effect on response. p Values and estimated coefficients of the significant terms for whiteness, tensile strength, and tear strength are given in Tables 3, 4, and 5, respectively. A higher value of the coefficient indicates a stronger effect of the corresponding term and vice versa. A plus sign (+) or no sign indicates that the response increases with increasing factor value and vice versa.

Table 3.

Analysis of Variance and Estimated Regression Coefficients for Whiteness.

	Linear Effect			Square Effect			Interaction Effect
Terms	X ₁—KMnO₄ (g/kg)	X ₂—pH	X ₃—Time (min)	$X_{1}^{2}$	$X_{2}^{2}$	$X_{3}^{2}$	X ₁ X ₂	X ₂ X ₃	X ₁ X ₃
p Value	.030	.000	.000	.000	—	—	.030	—	—
Coefficient	−0.5725	−4.7850	1.5412	−1.3867	—	—	0.8100	—	—

Note. KMnO₄ = potassium permanganate.

Table 4.

Analysis of Variance and Estimated Regression Coefficients for Tensile Strength.

	Linear Effect			Square Effect			Interaction Effect
Terms	X ₁—KMnO₄ (g/kg)	X ₂—pH	X ₃—Time (min)	$X_{1}^{2}$	$X_{2}^{2}$	$X_{3}^{2}$	X ₁ X ₂	X ₂ X ₃	X ₁ X ₃
Warp tensile strength
p Value	.000	.000	.002	—	.016	—	.034	—	—
Coefficient	−52.332	21.709	−9.524	—	−4.264	—	−6.425	—	—
Weft tensile strength
p Value	.000	.000	.002	—	—	—	.045	—	—
Coefficient	−41.966	21.684	−8.494	—	—	—	−6.445	—	—

Note. KMnO₄ = potassium permanganate.

Table 5.

Analysis of Variance and Estimated Regression Coefficients Tear Strength.

	Linear Effect			Square Effect			Interaction Effect
Terms	X ₁—KMnO₄ (g/kg)	X ₂—pH	X ₃—Time (min)	$X_{1}^{2}$	$X_{2}^{2}$	$X_{3}^{2}$	X ₁ X ₂	X ₂ X ₃	X ₁ X ₃
Warp tear strength
p Values	.000	.000	.002	—	—	—	.005	—	—
Coefficient	−314.81	175.19	−93.19	—	—	—	112.87	—	—
Weft tear strength
p Values	.000	.000	.009	.048	—	—	.007	—	—
Coefficient	−153.00	96.75	−52.62	−15.58	—	—	41.75	—	—

Note. KMnO₄ = potassium permanganate.

The optimization of response variables was done by a response optimizer tool, which is used to find input variable settings at which the response variables are optimized for desirable properties. Multiple response variables can be optimized at desirable settings in a single stage.

The surface plot in Figure 1 indicates that whiteness is directly proportional to reaction time (i.e., the time given to printed fabric before washing). This is also evident from the p value of .00 for time (Table 3), which shows that time has a significant effect on whiteness. Enhancement in whiteness by increasing reaction time is a manifestation of permitting longer contact time for the formation of MnO₂, by degradation of the dye chromophore. The KMnO₄ oxidizes the dye chromophore to make it colorless and converts itself to MnO₂. The indigo dye converts to isatin and to anthanilic acid by oxidation using KMnO₄ (Poulin, 2007).

Figure 1.

Surface plot of whiteness versus time and potassium permanganate concentration.

On the other hand, the KMnO₄ concentration in its relationship to whiteness shows the presence of curvature. By increasing the KMnO₄ concentration to a certain limit, whiteness of the fabric increases gradually; beyond that limit, an increase in KMnO₄ concentration results in a gradual decrease in whiteness. This trend of decreasing whiteness by increasing the KMnO₄ concentration beyond a certain level could be interpreted in terms of the deposition of a higher amount of MnO₂ on the fabric. Due to the colloidal nature of MnO₂, diffusion of oxygen from paste to fabric structure is impeded, thereby decreasing the whiteness of the fabric. However, the effect of reaction time on the increase in whiteness is greater than that of the KMnO₄ concentration, which is also evident from the p values (.000 for time and .030 for KMnO₄) and coefficient values (1.5412 for time and −0.5725 for KMnO₄), as given in Table 3.

A statistically significant curvature/square effect (X ²) could be found through ANOVA for whiteness, as given in Table 3. It implies that the whiteness discharge effect is not directly proportional to the KMnO₄ concentration in a linear manner, but an optimum middle level of KMnO₄ concentration needs to be used for an optimum white discharge effect.

The ASTM whiteness increased as printing paste pH decreased toward the acidic direction. It had been reported that the KMnO₄ oxidation potential depends upon the pH value. The oxidation potential (E _o) of KMnO₄ increases by decreasing pH, and E _o is much higher at an acidic pH (+1.70 V) than at an alkaline pH (+0.59 V; Dash, Patel, & Mishra, 2009; Shaabani, Tavasoli-Rad, & Lee, 2005). The effect of pH was found to be linear and inversely proportional to whiteness. Under controlled acidic conditions and in the presence of an excess amount of reducing agent, soluble manganese salts are formed resulting in effective bleaching (Alsberg & Liquorice, 1962). Although whiteness increases with an increase in acidity of the printing paste, there is a risk of loss in fabric strength under strong acidic conditions. As shown in Figure 2, the fabric whiteness increases with an increase in application duration of the printing paste before being washed off.

Figure 2.

Surface plot of whiteness versus pH and potassium permanganate concentration.

It can be seen from Figure 3 that both the KMnO₄ concentration and reaction time have significant effects on the tensile strength of denim fabric. The effect of KMnO₄ concentration (p = .000 and coefficient = −52.332 for warp; p = .000 and coefficient = −41.966 for weft; Table 4) is more evident in the surface plot by steepness of the KMnO₄ surface as compared to time (p = .000 and coefficient = −9.524 for warp; p = .002 and coefficient = −8.494 for weft; Table 4) with respect to warp way and weft way tensile strengths of the denim fabric. The KMnO₄ concentration had an inverse relationship with tensile strength (i.e., increasing the KMnO₄ concentration decreased tensile strength). This decrease in tensile strength might be due to a decrease in the degree of polymerization of cellulose as a result of oxidation by KMnO₄. Similarly, an increase in reaction time resulted in a decrease in tensile strength. The decrease in tensile strength by increase in duration of reaction indicates the progressive degradation of cellulose molecules.

Figure 3.

Surface plot of tensile and tear strength versus time and potassium permanganate concentration.

Tensile strength decreased by increasing the KMnO₄ concentration due to the aggressive oxidation. When the pH of the paste became more acidic, a decrease in tensile strength was observed. This means that the KMnO₄ concentration (coefficient = −52.332 for warp, coefficient = −41.966 for weft; Table 4) had an inverse relationship, while pH (coefficient = 21.709 for warp, coefficient = 21.684 for weft; Table 4) had a direct relationship with the warp way and weft way tensile strengths of the denim fabric. A statistically significant interaction was found between the KMnO₄ concentration and pH (p = .03 and coefficient = −6.425 for warp; p = .045 and coefficient = −6.445 for weft; Table 4): The effect of KMnO₄ was found to be dependent on the pH of the printing paste. At a lower pH, the effect of increasing the KMnO₄ concentration was considerably significant in reducing the fabric tensile strength. The steepness of the KMnO₄ surface for different pH values was probably due to the breakage of cellulose chains, which occurs when pH is decreased and the KMnO₄ concentration is increased, resulting in a great decrease in tensile strength. A similar trend can be observed in the case of fabric tear strength (Figures 3 and 4). Application duration, before washing off, also results in decreasing both the fabric tensile and tear strength. A scanning electron micrograph shown in Figure 5 depicts the oxidative-damaged cotton fibers after discharge printing.

Figure 4.

Surface plot of tensile and tear strength versus printing paste pH and potassium permanganate concentration.

Figure 5.

Scanning electron micrograph of unprinted (right) and discharged printed fabric (left).

It is also evident from the ANOVA given in Tables 4 and 5 that the effect of all three experimental factors (i.e., KMnO₄ concentration, pH, and application duration) was statistically significant on the fabric tensile and tear strength. Fabric tensile strength decreased with an increase in KMnO₄ concentration at an acidic pH (Figure 4).

Models were developed from the ANOVA that represents the dependence of response variables on KMnO₄ concentration (X ₁), pH (X ₂), and time (X ₃), as given in Table 6. The R ² (%) values represent the percentage of change in the response variables that can be explained by the terms included in the model equations. It is clear that more than 98% of the variation in discharge-printed fabric whiteness, as well as fabric tensile and tear strengths, can be explained by variation in the experimental factors, their interactions, and square terms included in the models.

Table 6.

Quadratic Models for Predicting the Response Variables.

Response		Equation	R ² (%)
Discharge whiteness		$\begin{array}{l} 68.0465 + 0.783074 (X_{1}) - 6.48000 (X_{2}) + 1.72125 (X_{3}) \\ - 0.0132632 (X_{1}^{2}) + 0.0810000 (X_{1} X_{2}) - 0.235500 (X_{2} X_{3}) \end{array}$	98.00
Tensile strength	Warp	$\begin{array}{l} 345.792 - 1.37825 (X_{1}) + 104.666 (X_{2}) - 1.90475 (X_{3}) \\ - 4.23603 (X_{2}^{2}) - 0.642500 (X_{1} X_{2}) \end{array}$	98.16
Tensile strength	Weft	$\begin{array}{l} 337.490 - 0.329625 (X_{1}) + 53.9088 (X_{2}) - 1.69875 (X_{3}) \\ - 0.644500 (X_{1} X_{2}) \end{array}$	97.69
Tear strength	Warp	$\begin{array}{l} 9, 633.76 - 99.2062 (X_{1}) - 389.187 (X_{2}) \\ - 18.6375 (X_{3}) + 11.2875 (X_{1} X_{2}) \end{array}$	93.78
Tear strength	Weft	$\begin{array}{l} 6, 253.56 - 27.2471 (X_{1}) - 112.000 (X_{2}) - 10.5250 (X_{3}) \\ - 0.131029 (X_{1}^{2}) + 4.17500 (X_{1} X_{2}) \end{array}$	96.94

Note. X ₁ = KMnO₄, X ₂ = pH, X ₃ = Time. KMnO₄ = potassium permanganate.

A multiple response optimization approach was adopted for the optimization of discharge printing with KMnO₄ using Minitab’s response optimizer tool to find input variable settings at which the response variables were to be optimized. All three response variables (i.e., discharge whiteness, fabric tensile strength, and fabric tear strength) were optimized simultaneously into a single multiresponse optimization model. The overall optimization goal was to maximize the whiteness effect of discharge while minimizing the loss in fabric tensile and tear strength. The optimum process conditions determined by the response optimizer were found as follows: a KMnO₄ concentration of 42 g/kg, a pH of 6, and a reaction time of 15 min, which provide 64.41 whiteness, 565.99 N and 455.69 N warp way and weft way tensile strengths, and 5,693.17 cN and 5,099.49 cN warp way and weft way tear strengths, respectively (Figure 6). After determining the optimum process conditions, confirmatory experiments were also run. The predictability of the optimal recipe determined by the response optimizer was found satisfactory in terms of low absolute error (approx. 5%; Table 7).

Figure 6.

Response optimizer plot.

Table 7.

Actual and Predicted Values of Response Variables.

Responses		Predicted	Actual	Error % age
Whiteness		64.42	62.12	3.55
Tensile strength (N)	Warp	565.99	548.10	3.16
Tensile strength (N)	Weft	455.70	481.87	5.4
Tear strength (cN)	Warp	5,693.17	5,742	0.85
Tear strength (cN)	Weft	5,099.49	5,265	3.24

Conclusions

The RSM has been employed to find the optimum concentration and conditions for discharge printing of denim fabric using KMnO₄ as an oxidative agent. All three input variables (KMnO₄ concentration, pH, and reaction time) were found to be statistically significant according to ANOVA. The CCD of RSM was successfully employed to develop quadratic models for predicting whiteness, tensile, and tear strengths of discharge-printed denim fabric. It was observed that discharge whiteness is directly proportional to reaction time and KMnO₄ concentration up to a certain level but then decreases. However, it is inversely proportional to pH (i.e., whiteness increases with decrease in pH toward the acidic side). Tensile and tear strengths were found to decrease with an increase in KMnO₄ concentration, reaction time, and decrease in pH. Using the response optimizer tool, the optimal concentration and conditions for KMnO₄ formulation were found to be a KMnO₄ concentration of 42g/kg, a pH of 6, and a reaction time of 15 min, which provided 65 ASTM whiteness, 565 N warp and 455 N weft tensile strengths, and 5,693 cN warp and 5,099 cN weft tear strengths. This is a cost-effective, clean approach to discharge print fashionable denim fabric. This technique has applications in textile design and in decolorizing the waste dyes, fabric waste, and wastewaters containing such dyes. But the discharging results using this method would likely be different with different samples of denim (based on the varying penetration of indigo into the warp yarns). Therefore, the results are applicable only to this particular sample, and trials should be made with respect to varying thickness and other fabric construction parameters.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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